A Tutorial on Model Predictive Control: Using a Linear Velocity-Form Model

摘要

Model Predictive Control (MPC) has a long history in the field of control engineering. It is one of the few areas that has received on-going interest from researchers in both industry and universities. It has been recognised that there are three major branches of MPC algorithms consisting of step-response model based design: Dynamic Matrix Control (DMC); transfer Junction model based design: Generalised Predictive Control (GPC); and a general state space model based design. The DMC and GPC algorithms can also be caste in the state space framework. Along the general lines of state space methods, there are two mainstreams: one solves for the optimal control signal while the other solves for the increment of the optimal control signal. The latter can be implemented in a velocity form analogous to the implementation of a PID controller on an industrial plant. Motivated by this advantage, and that integral action is naturally embedded in the algorithm, this tutorial paper focuses on an introduction to Model Predictive Control based on the state space approach using a linear velocity-form model.